Rollback for agent-made changes
An agent that can write needs a reverse for every write. Rollback is a design property, not an incident-time improvisation.
What is rollback for agent-made changes?
An agent that can write needs a reverse for every write. Rollback is a design property, not an incident-time improvisation.
Implementation
- Group a run's writes into a single change set with one identifier.
- Record an inverse operation for each write at the time the write is issued.
- Prefer additive changes and feature flags over destructive edits.
- Rehearse the rollback in a staging environment before the change ships.
- Keep the rollback path executable by a human without the agent.
Failure modes
- Rollback plans written as prose instead of executable steps.
- Destructive migrations with no captured pre-state.
How it is verified
- Execute the rollback in staging and diff the resulting state against the pre-change snapshot.
Questions this pattern answers
What is rollback for agent-made changes?
An agent that can write needs a reverse for every write. Rollback is a design property, not an incident-time improvisation.
How do you implement rollback for agent-made changes?
Group a run's writes into a single change set with one identifier. Record an inverse operation for each write at the time the write is issued. Prefer additive changes and feature flags over destructive edits. Rehearse the rollback in a staging environment before the change ships. Keep the rollback path executable by a human without the agent.
How is rollback for agent-made changes verified?
Execute the rollback in staging and diff the resulting state against the pre-change snapshot.
What usually goes wrong with rollback for agent-made changes?
Rollback plans written as prose instead of executable steps. Destructive migrations with no captured pre-state.
Projects in this track
ClawKeeper →
Governed Claude agent control plane. A deterministic policy engine runs before every LLM call, autonomous spend defaults to zero, and the audit log is enforced by the database.
TechTide Harness Kit →
Trust contracts for agent skills expressed as JSON Schema: risk tiers, tool permissions, deny-by-default egress, approval gates, and prompt-injection fixtures behind a wall of CI validation gates.
CipherClaw →
Deterministic multi-agent debugger with zero LLM calls in the analysis path: causal graphs, weighted root-cause scoring, and failure prediction.
Swarm 357 →
357 Claude agent roles across six business layers, with portable memory, enforced cost caps, a bash policy gate, and durable checkpoints.
Related patterns
This page documents how the pattern is built. Engagement scope and pricing live on TechTide AI.
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